tensorflow – tensorflow


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TensorFlow is an end-to-end open source platform
for machine learning. It has a comprehensive, flexible ecosystem of
tools,
libraries, and
community resources that lets
researchers push the state-of-the-art in ML and developers easily build and
deploy ML-powered applications.

TensorFlow was originally developed by researchers and engineers working within
the Machine Intelligence team at Google Brain to conduct research in machine
learning and neural networks. However, the framework is versatile enough to be
used in other areas as well.

TensorFlow provides stable Python
and C++ APIs, as well as a
non-guaranteed backward compatible API for
other languages.

Keep up-to-date with release announcements and security updates by subscribing
to
announce@tensorflow.org.
See all the mailing lists.

Install

See the TensorFlow install guide for the
pip package, to
enable GPU support, use a
Docker container, and
build from source.

To install the current release, which includes support for
CUDA-enabled GPU cards *(Ubuntu and
Windows)*:

 pip install tensorflow

Other devices (DirectX and MacOS-metal) are supported using
Device Plugins.

A smaller CPU-only TensorFlow package is also available:

 pip install tensorflow-cpu

To update TensorFlow to the latest version, add the --upgrade flag to the
commands above.

*Nightly binaries are available for testing using the
tf-nightly and
tf-nightly-cpu packages on PyPI.*

Try your first TensorFlow program

$ python

>>> import tensorflow as tf
>>> tf.add(1, 2).numpy()
3
>>> hello = tf.constant('Hello, TensorFlow!')
>>> hello.numpy()
b'Hello, TensorFlow!'

For more examples, see the
TensorFlow Tutorials.

Contribution guidelines

**If you want to contribute to TensorFlow, be sure to review the
Contribution Guidelines. This project adheres to TensorFlow's
Code of Conduct. By participating, you are expected to
uphold this code.**

**We use GitHub Issues for
tracking requests and bugs, please see
TensorFlow Forum for general questions and
discussion, and please direct specific questions to
Stack Overflow.**

The TensorFlow project strives to abide by generally accepted best practices in
open-source software development.

Patching guidelines

Follow these steps to patch a specific version of TensorFlow, for example, to
apply fixes to bugs or security vulnerabilities:

• Clone the TensorFlow repository and switch to the appropriate branch for

your desired version—for example, r2.8 for version 2.8.

• Apply the desired changes (i.e., cherry-pick them) and resolve any code

conflicts.

• Run TensorFlow tests and ensure they pass.
• Build the TensorFlow pip

package from source.

Continuous build status

You can find more community-supported platforms and configurations in the
TensorFlow SIG Build Community Builds Table.

Official Builds

Build Type | Status | Artifacts
----------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ---------
Linux CPU |  | PyPI
Linux GPU |  | PyPI
Linux XLA |  | TBA
macOS |  | PyPI
Windows CPU |  | PyPI
Windows GPU |  | PyPI
Android |  | Download
Raspberry Pi 0 and 1 |  | Py3
Raspberry Pi 2 and 3 |  | Py3

Resources

• TensorFlow.org
• TensorFlow Tutorials
• TensorFlow Official Models
• TensorFlow Examples
• TensorFlow Codelabs
• TensorFlow Blog
• Learn ML with TensorFlow
• TensorFlow Twitter
• TensorFlow YouTube
• TensorFlow model optimization roadmap
• TensorFlow White Papers
• TensorBoard Visualization Toolkit
• TensorFlow Code Search

Learn more about the
TensorFlow Community and how to
Contribute.

Courses

• Coursera
• Udacity
• Edx

License

Apache License 2.0